Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (4): 1196-1201.DOI: 10.11772/j.issn.1001-9081.2019091539

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Improved fuzzy c-means MRI segmentation based on neighborhood information

WANG Yan, HE Hongke   

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2019-09-05 Revised:2019-11-04 Online:2020-04-10 Published:2019-11-18


王燕, 何宏科   

  1. 兰州理工大学 计算机与通信学院, 兰州 730050
  • 通讯作者: 何宏科
  • 作者简介:王燕(1971-),女,甘肃兰州人,教授,硕士,CCF会员,主要研究方向:模式识别、图像处理;何宏科(1993-),男,甘肃平凉人,硕士研究生,主要研究方向:模式识别、图像处理。

Abstract: In the segmentation of brain image,the image quality is often reduced due to the influence of noise or outliers. And traditional fuzzy clustering has some limitations and is easily affected by the initial value,which brings great trouble for doctors to accurately identify and extract brain tissue. Aiming at these problems,an improved fuzzy clustering image segmentation method based on neighborhoods of image pixels constructed by Markov model was proposed. Firstly,the initial clustering center was determined by Genetic Algorithm(AG). Secondly,the expression of the target function was changed,the calculation method of the membership matrix was changed by adding the correction term in the target function and was adjusted by the constraint coefficient. Finally,the Markov Random Field(WRF)was used to represent the label information of the neighborhood pixels,and the maximized conditional probability of Markov random field was used to represent the neighborhood of the pixel,which improves the noise immunity. Experimental results show that the proposed method has good noise immunity,it can reduce the false segmentation rate and has high segmentation accuracy when used to segment brain images. The average accuracy of the segmented image has Jaccard Similarity(JS)index of 82. 76%,Dice index of 90. 45%,and Sensitivity index of 90. 19%. At the same time,the segmentation of brain image boundaries is clearer and the segmented image is closer to the standard segmentation image.

Key words: Fuzzy c-Means (FCM) clustering, Magnetic Resonance Image (MRI), neighborhood information, Markov Random Field (MRF), Genetic Algorithm (GA)

摘要: 在脑图像分割中,噪声或异常值的干扰往往会使得图像的质量下降。而传统的模糊c均值算法存在一定的缺限,容易受初始值的影响,这给医生准确识别和提取脑组织带来很大的麻烦。针对这些问题,提出一种基于用马尔可夫模型构建的图像像素点邻域的改进模糊c均值图像分割方法。首先,用遗传算法(GA)确定初始的聚类中心;然后,改变目标函数的表达方式,通过在目标函数中添加修正项来改变隶属度矩阵的计算方式,并用约束系数对其来调节;最后,由马尔可夫随机域来表达邻域像素的标号信息,并利用马尔可夫随机场(MRF)的最大化条件概率来表示像素的邻域,增强了抗噪性。实验结果显示,该方法拥有较好的抗噪性,可以降低误分割率,在对脑图像分割时具备较高的分割精度。分割后的图像平均精度可达:JS(Jaccard Similarity)指标为82.76%,Dice指标为90.45%,Sensitivity指标为90.19%;同时,对脑图像边界处的分割更加清晰,分割后的图像更加接近于标准分割图像。

关键词: 模糊c均值聚类, 磁共振影像, 邻域信息, 马尔可夫随机场, 遗传算法

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